Articles | Volume 23, issue 16
https://doi.org/10.5194/acp-23-9071-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-9071-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2023
Research article |
| 19 Sep 2023
| 19 Sep 2023
Automated detection and monitoring of methane super-emitters using satellite data
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
GHGSat Inc., Montreal, Canada
Joannes D. Maasakkers
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Pieter Bijl
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Gourav Mahapatra
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Anne-Wil van den Berg
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
now at: Department of Meteorology and Air Quality, Wageningen University, Wageningen, the Netherlands
Sudhanshu Pandey
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
now at: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Alba Lorente
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Tobias Borsdorff
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Sander Houweling
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Daniel J. Varon
GHGSat Inc., Montreal, Canada
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Jason McKeever
GHGSat Inc., Montreal, Canada
Dylan Jervis
GHGSat Inc., Montreal, Canada
Marianne Girard
GHGSat Inc., Montreal, Canada
Itziar Irakulis-Loitxate
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
International Methane Emission Observatory, United Nations Environment Program, Paris, France
Javier Gorroño
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV), Valencia, Spain
Environmental Defense Fund, Amsterdam, the Netherlands
Daniel H. Cusworth
Carbon Mapper, Inc., Pasadena, CA, USA
Arizona Institute for Resilience, University of Arizona, Tucson, AZ, USA
Ilse Aben
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Department of Earth Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Related authors
No articles found.
Jack H. Bruno, Daniel J. Jacob, Xiaolin Wang, Melissa P. Sulprizio, Lucas A. Estrada, Daniel J. Varon, Steven C. Wofsy, Mark Omara, and Ritesh Gautam
EGUsphere, https://doi.org/10.5194/egusphere-2025-4626, https://doi.org/10.5194/egusphere-2025-4626, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We use data from both aircraft (MethaneAIR) and satellite (TROPOMI) based measurements of methane in the atmosphere to better understand methane emissions from fossil fuel extraction. Data from these instruments are combined with a computer model of the atmosphere to improve estimates of methane emissions. We find that combining data from multiple sources provides more information than either source on its own. The tools and data we use are freely available.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida B. M. Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre A. G. Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido van der Werf, Sebastiaan J. van de Velde, Anthony Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-659, https://doi.org/10.5194/essd-2025-659, 2025
Preprint under review for ESSD
Short summary
Short summary
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Andrew Gerald Barr, Jochen Landgraf, Mari Martinez-Velarte, Mihalis Vrekoussis, Ralf Sussmann, Isamu Morino, Kimberly Strong, Minqiang Zhou, Voltaire A. Velazco, Hirofumi Ohyama, Thorsten Warneke, Frank Hase, and Tobias Borsdorff
Atmos. Meas. Tech., 18, 6093–6123, https://doi.org/10.5194/amt-18-6093-2025, https://doi.org/10.5194/amt-18-6093-2025, 2025
Short summary
Short summary
The Greenhouse Gases Observing Satellite-2 (GOSAT-2) is a satellite dedicated to measuring concentrations of greenhouse gases from space. Since its launch, the increase of CH4 and CO2 concentrations in the atmosphere is clear. The datasets obtained from GOSAT-2 are used in the Copernicus atmospheric services to monitor the climate, in light of the Paris Agreement. Here we present robust datasets of these gases from GOSAT-2, including a novel machine learning approach to data quality filtering.
Drew C. Pendergrass, Daniel J. Jacob, Nicholas Balasus, Lucas Estrada, Daniel J. Varon, James D. East, Megan He, Todd A. Mooring, Elise Penn, Hannah Nesser, and John R. Worden
Atmos. Chem. Phys., 25, 14353–14369, https://doi.org/10.5194/acp-25-14353-2025, https://doi.org/10.5194/acp-25-14353-2025, 2025
Short summary
Short summary
We use satellite observations of atmospheric methane, a greenhouse gas, to calculate emissions from both human and natural sources. We find that methane emissions surged in 2020–2021 before declining in 2022–2023. We attribute the surge in large part to emissions from eastern Africa, which experienced large methane-generating floods. Wetland models underestimate emissions in that region, which has led some previous work to incorrectly attribute the African surge in methane emissions to livestock.
Javier Roger, Luis Guanter, and Javier Gorroño
Atmos. Meas. Tech., 18, 5545–5567, https://doi.org/10.5194/amt-18-5545-2025, https://doi.org/10.5194/amt-18-5545-2025, 2025
Short summary
Short summary
Detection of methane point source emissions with satellite-based imaging spectrometers is assessed in offshore areas. After integrating simulations into data from 2 different satellite missions, we extract the probability of detection. We compare our results with real plumes, which shows the consistency of the models. Detection and monitoring efforts can be optimized by filtering data based on these results, reducing unnecessary searches and increasing the action taken on these emissions.
Javier Gorroño, Zhipeng Pei, Adriana Valverde, and Luis Guanter
EGUsphere, https://doi.org/10.5194/egusphere-2025-4924, https://doi.org/10.5194/egusphere-2025-4924, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Methane plumes can be detected with several instruments from space at a high spatial resolution nowadays. We see a ground projection of these methane plumes from the satellites that, similarly to clouds or buildings, are distorted depending on the observation and illumination angle. Here we highlight this issue and propose a methodology to account for it using simulations that could enhance current and upcoming retrieval and quantification algorithms.
Luis Guanter, Javier Roger, Jack Warren, Maryann Sargent, Zhan Zhang, Sébastien Roche, Christopher Chan Miller, Michael Steiner, Harvey Hadfield, Mark Omara, James P. Williams, Katlyn MacKay, Jonathan E. Franklin, Steven C. Wofsy, Steven P. Hamburg, and Ritesh Gautam
EGUsphere, https://doi.org/10.5194/egusphere-2025-4666, https://doi.org/10.5194/egusphere-2025-4666, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
MethaneSAT was a satellite mission which generated data on methane emissions. In this work, we evaluate the potential of MethaneSAT to detect and quantify methane plumes, and use the existing data archive to evaluate the methane super-emissions from the most important oil and gas basins in the world.
Jack D. Warren, Maryann Sargent, James P. Williams, Mark Omara, Christopher C. Miller, Sebastien Roche, Katlyn MacKay, Ethan Manninen, Apisada Chulakadabba, Anthony Himmelberger, Joshua Benmergui, Zhan Zhang, Luis Guanter, Steve Wofsy, and Ritesh Gautam
Atmos. Chem. Phys., 25, 10661–10675, https://doi.org/10.5194/acp-25-10661-2025, https://doi.org/10.5194/acp-25-10661-2025, 2025
Short summary
Short summary
Mitigating anthropogenic methane emissions requires a detailed understanding of emitting facilities. We use observations of methane point sources from the MethaneAIR instrument from 2021–2023 that covered ~80 % of US onshore oil and gas production regions. We attribute these observations to facility types to explore how emissions vary by industrial sectors. Oil and gas facilities make up most point source emissions nationally, but in certain basins other sectors can make up the majority.
Kanwal Shahzadi, Matthias Schneider, Nga Ying Lo, Frank Hase, Jörg Meyer, Ugur Cayoglu, Tobias Borsdorff, and Mari C. Martinez-Velarte
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-407, https://doi.org/10.5194/essd-2025-407, 2025
Preprint under review for ESSD
Short summary
Short summary
We merge TROPOMI and IASI L2 methane data from January 2018 to June 2021 on a global scale. The data merging yields total column-averaged and upper tropospheric/stratospheric partial column-averaged mole fractions (already provided by the individual data sets). In addition, it outputs tropospheric partial column-averaged mole fractions (not provided by any of the two individual data sets). We briefly document their increased potential for quantifying near-surface methane emissions.
Luis Guanter, Jack Warren, Mark Omara, Apisada Chulakadabba, Javier Roger, Maryann Sargent, Jonathan E. Franklin, Steven C. Wofsy, and Ritesh Gautam
Atmos. Meas. Tech., 18, 3857–3872, https://doi.org/10.5194/amt-18-3857-2025, https://doi.org/10.5194/amt-18-3857-2025, 2025
Short summary
Short summary
This study presents a data processing scheme for the detection and quantification of methane emissions using the MethaneAIR airborne spectrometer. We show that the proposed methods enable the detection of smaller plumes (compared with those detectable using other existing methods) and improve the potential of MethaneAIR to survey methane point sources across large regions.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025, https://doi.org/10.5194/gmd-18-4571-2025, 2025
Short summary
Short summary
We introduce a new simulation platform based on the Dutch Atmospheric Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in turbulent environments at a hectometer resolution. This model incorporates both anthropogenic emission inventories and online ecosystem fluxes. Simulation results for the main urban area in the Netherlands demonstrate the strong potential of DALES to improve CO2 emission modeling and to support mitigation strategies.
Katlyn MacKay, Joshua Benmergui, James P. Williams, Mark Omara, Anthony Himmelberger, Maryann Sargent, Jack D. Warren, Christopher C. Miller, Sébastien Roche, Zhan Zhang, Luis Guanter, Steven Wofsy, and Ritesh Gautam
EGUsphere, https://doi.org/10.5194/egusphere-2025-3008, https://doi.org/10.5194/egusphere-2025-3008, 2025
Short summary
Short summary
Reducing methane emissions from the oil/gas sector can mitigate near-term climate warming and the losses of a valuable energy resource. We analyze data collected using MethaneAIR to assess methane emissions in regions accounting for 70 % of United States onshore oil/gas production in 2023. We estimate total oil/gas methane emissions across all measured regions to be ~8 Tg/yr, equivalent to 1.6 % of produced gas, which is five times higher than reported by the US Environmental Protection Agency.
Aurélien Sicsik-Paré, Audrey Fortems-Cheiney, Isabelle Pison, Grégoire Broquet, Alvin Opler, Elise Potier, Adrien Martinez, Oliver Schneising, Michael Buchwitz, Joannes D. Maasakkers, Tobias Borsdorff, and Antoine Berchet
EGUsphere, https://doi.org/10.5194/egusphere-2025-2622, https://doi.org/10.5194/egusphere-2025-2622, 2025
Short summary
Short summary
Assimilating satellite observations from TROPOMI provides top-down quantification of regional methane emissions. This study compares European emissions in 2019 estimated from the inversion of three TROPOMI datasets. We find inconsistencies in national budgets and spatial patterns, with no product clearly superior. We disentangle drivers of the differences, highlighting the impact of differences in coverage, observations and associated errors on the consistency of methane emission estimates.
Hannah Nesser, Kevin W. Bowman, Matthew D. Thill, Daniel J. Varon, Cynthia A. Randles, Ashutosh Tewari, Felipe J. Cardoso-Saldaña, Emily Reidy, Joannes D. Maasakkers, and Daniel J. Jacob
EGUsphere, https://doi.org/10.5194/egusphere-2025-2850, https://doi.org/10.5194/egusphere-2025-2850, 2025
Short summary
Short summary
Regional analyses of atmospheric trace gases can improve knowledge of fluxes and their trends at high resolution but rely on the specification of boundary conditions at the domain edges. Biases in the often-uncertain boundary conditions propagate to the inferred fluxes. We develop a framework to explain how errors in the boundary conditions influence the optimized fluxes, derive two metrics to estimate this influence, and compare two methods to correct for the biases.
Eleftherios Ioannidis, Antoon Meesters, Michael Steiner, Dominik Brunner, Friedemann Reum, Isabelle Pison, Antoine Berchet, Rona Thompson, Espen Sollum, Frank-Thomas Koch, Christoph Gerbig, Fenjuan Wang, Shamil Maksyutov, Aki Tsuruta, Maria Tenkanen, Tuula Aalto, Guillaume Monteil, Hong Lin, Ge Ren, Marko Scholze, and Sander Houweling
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-235, https://doi.org/10.5194/essd-2025-235, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
This paper describes a detailed study on CH4 European emissions, using different methodologies (9 total inverse models). The study spans over 15 years and provides detailed information on European CH4 emission trends and seasonality, using in-situ data, including ICOS network. Our results highlight the importance of improving details in the inversion setup, such as the treatment of lateral boundary conditions to narrow the uncertainty ranges further.
Bibhasvata Dasgupta, Malika Menoud, Carina van der Veen, Ingeborg Levin, Cora Veidt, Heiko Moossen, Sylvia Englund Michel, Peter Sperlich, Shinji Morimoto, Ryo Fujita, Taku Umezawa, Stephen Matthew Platt, Christine Groot Zwaaftink, Cathrine Lund Myhre, Rebecca Fisher, David Lowry, Euan Nisbet, James France, Ceres Woolley Maisch, Gordon Brailsford, Rowena Moss, Daisuke Goto, Sudhanshu Pandey, Sander Houweling, Nicola Warwick, and Thomas Röckmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2439, https://doi.org/10.5194/egusphere-2025-2439, 2025
Short summary
Short summary
We combined long-term methane mole fraction and isotope measurements from eight laboratories that sample high-latitude stations to compare, offset correct and harmonise the datasets into a hemisphere merged timeseries. Because each laboratory uses slightly different methods, we adjusted the data to make it directly comparable. This allowed us to create a consistent record of atmospheric methane concentration and its isotopes from 1988 to 2023.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Riley Duren, Daniel Cusworth, Alana Ayasse, Kate Howell, Alex Diamond, Tia Scarpelli, Jinsol Kim, Kelly O'neill, Judy Lai-Norling, Andrew Thorpe, Sander R. Zandbergen, Lucas Shaw, Mark Keremedjiev, Jeff Guido, Paul Giuliano, Malkam Goldstein, Ravi Nallapu, Geert Barentsen, David R. Thompson, Keely Roth, Daniel Jensen, Michael Eastwood, Frances Reuland, Taylor Adams, Adam Brandt, Eric A. Kort, James Mason, and Robert O. Green
EGUsphere, https://doi.org/10.5194/egusphere-2025-2275, https://doi.org/10.5194/egusphere-2025-2275, 2025
Short summary
Short summary
We describe the Carbon Mapper emissions monitoring system including methane and carbon dioxide observations from the constellation of Tanager hyperspectral satellites, a global monitoring strategy optimized for enabling mitigation impact at the scale of individual facilities, and a data platform that delivers timely and transparent information for diverse stakeholders. We present early findings from Tanager-1 including the use of our data to locate and repair a leaking oil and gas pipeline.
Shutao Zhao, Yuzhong Zhang, Shuang Zhao, Xinlu Wang, and Daniel J. Varon
Atmos. Chem. Phys., 25, 4035–4052, https://doi.org/10.5194/acp-25-4035-2025, https://doi.org/10.5194/acp-25-4035-2025, 2025
Short summary
Short summary
We target the challenge of detecting methane super-emitters in oil and gas fields, which is critical for mitigating climate change. Traditional satellite-based detectors struggle due to interference from complex surfaces. We developed a novel method using deep transfer learning that improves detection efficiency and accuracy by reducing artifacts and adapting methane knowledge to different regions. Application revealed significant methane emissions, demonstrating the potential of our method.
Elise Penn, Daniel J. Jacob, Zichong Chen, James D. East, Melissa P. Sulprizio, Lori Bruhwiler, Joannes D. Maasakkers, Hannah Nesser, Zhen Qu, Yuzhong Zhang, and John Worden
Atmos. Chem. Phys., 25, 2947–2965, https://doi.org/10.5194/acp-25-2947-2025, https://doi.org/10.5194/acp-25-2947-2025, 2025
Short summary
Short summary
The hydroxyl radical (OH) destroys many air pollutants, including methane. Global-mean OH cannot be directly measured, and thus it is inferred with the methyl chloroform (MCF) proxy. MCF is decreasing, and a replacement is needed. We use satellite observations of methane in two spectral ranges as a proxy for OH. We find shortwave infrared observations can characterize yearly OH and its seasonality but not the latitudinal distribution. Thermal infrared observations add little information.
Tia R. Scarpelli, Elfie Roy, Daniel J. Jacob, Melissa P. Sulprizio, Ryan D. Tate, and Daniel H. Cusworth
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-552, https://doi.org/10.5194/essd-2024-552, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
We present an update of the Global Fuel Exploitation Inventory (GFEI), a global inventory of methane emissions from oil, gas, and coal exploitation. GFEI v3 uses emissions as reported by countries in national inventories for 2020, and new infrastructure information, including a new dataset on coal mine locations. The goal of updating GFEI is to provide a more accurate spatial representation of the country-reported national inventories, allowing comparison with methane monitoring data.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Gijs Leguijt, Joannes D. Maasakkers, Hugo A. C. Denier van der Gon, Arjo J. Segers, Tobias Borsdorff, Ivar R. van der Velde, and Ilse Aben
Atmos. Chem. Phys., 25, 555–574, https://doi.org/10.5194/acp-25-555-2025, https://doi.org/10.5194/acp-25-555-2025, 2025
Short summary
Short summary
The production of steel coincides with large emissions of greenhouse gases and air pollutants including carbon monoxide. European facilities are required to report their emissions, which are estimated using a variety of methods. We evaluate these estimates using carbon monoxide concentrations measured via satellite. We find generally good agreement between our values and those reported but also identify some uncertainties, showing that satellites can provide insights into these emissions.
Sarah E. Hancock, Daniel J. Jacob, Zichong Chen, Hannah Nesser, Aaron Davitt, Daniel J. Varon, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, María Cazorla, Laura Dawidowski, Sebastián Diez, James D. East, Elise Penn, Cynthia A. Randles, John Worden, Ilse Aben, Robert J. Parker, and Joannes D. Maasakkers
Atmos. Chem. Phys., 25, 797–817, https://doi.org/10.5194/acp-25-797-2025, https://doi.org/10.5194/acp-25-797-2025, 2025
Short summary
Short summary
We quantify 2021 methane emissions in South America at up to 25 km × 25 km resolution using satellite methane observations. We find a 55 % upward adjustment to anthropogenic emission inventories, including those reported to the UN Framework Convention on Climate Change under the Paris Agreement. Our estimates match inventories for Brazil, Bolivia, and Paraguay but are much higher for other countries. Livestock emissions (65 % of anthropogenic emissions) show the largest discrepancies.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, https://doi.org/10.5194/essd-16-4325-2024, 2024
Short summary
Short summary
This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Brian Nathan, Joannes D. Maasakkers, Stijn Naus, Ritesh Gautam, Mark Omara, Daniel J. Varon, Melissa P. Sulprizio, Lucas A. Estrada, Alba Lorente, Tobias Borsdorff, Robert J. Parker, and Ilse Aben
Atmos. Chem. Phys., 24, 6845–6863, https://doi.org/10.5194/acp-24-6845-2024, https://doi.org/10.5194/acp-24-6845-2024, 2024
Short summary
Short summary
Venezuela's Lake Maracaibo region is notoriously hard to observe from space and features intensive oil exploitation, although production has strongly decreased in recent years. We estimate methane emissions using 2018–2020 TROPOMI satellite observations with national and regional transport models. Despite the production decrease, we find relatively constant emissions from Lake Maracaibo between 2018 and 2020, indicating that there could be large emissions from abandoned infrastructure.
Qiurun Yu, Dylan Jervis, and Yi Huang
Atmos. Meas. Tech., 17, 3347–3366, https://doi.org/10.5194/amt-17-3347-2024, https://doi.org/10.5194/amt-17-3347-2024, 2024
Short summary
Short summary
This study estimated the effects of aerosols on GHGSat satellite methane retrieval and investigated the performance of simultaneously retrieving aerosol and methane information using a multi-angle viewing method. Results suggested that the performance of GHGSat methane retrieval improved when aerosols were considered, and the multi-angle viewing method is insensitive to the satellite angle setting. This performance assessment is useful for improving future GHGSat-like instruments.
Matthieu Dogniaux, Joannes D. Maasakkers, Daniel J. Varon, and Ilse Aben
Atmos. Meas. Tech., 17, 2777–2787, https://doi.org/10.5194/amt-17-2777-2024, https://doi.org/10.5194/amt-17-2777-2024, 2024
Short summary
Short summary
We analyze Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the 2022 Nord Stream 2 methane leak and show how challenging this case is for usual data analysis methods. We provide customized calibrations for this Nord Stream 2 case and assess that no firm conclusion can be drawn from L8 or S-2B single overpasses. However, if we opportunistically assume that L8 and S-2B results are independent, we find an averaged L8 and S-2B combined methane leak rate of 502 ± 464 t h−1.
Jack H. Bruno, Dylan Jervis, Daniel J. Varon, and Daniel J. Jacob
Atmos. Meas. Tech., 17, 2625–2636, https://doi.org/10.5194/amt-17-2625-2024, https://doi.org/10.5194/amt-17-2625-2024, 2024
Short summary
Short summary
Methane is a potent greenhouse gas and a current high-priority target for short- to mid-term climate change mitigation. Detection of individual methane emitters from space has become possible in recent years, and the volume of data for this task has been rapidly growing, outpacing processing capabilities. We introduce an automated approach, U-Plume, which can detect and quantify emissions from individual methane sources in high-spatial-resolution satellite data.
Anna Vaughan, Gonzalo Mateo-García, Luis Gómez-Chova, Vít Růžička, Luis Guanter, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 2583–2593, https://doi.org/10.5194/amt-17-2583-2024, https://doi.org/10.5194/amt-17-2583-2024, 2024
Short summary
Short summary
Methane is a potent greenhouse gas that has been responsible for around 25 % of global warming since the industrial revolution. Consequently identifying and mitigating methane emissions comprise an important step in combating the climate crisis. We develop a new deep learning model to automatically detect methane plumes from satellite images and demonstrate that this can be applied to monitor large methane emissions resulting from the oil and gas industry.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, https://doi.org/10.5194/acp-24-5069-2024, 2024
Short summary
Short summary
We quantify 2019 methane emissions in the contiguous US (CONUS) at a ≈ 25 km × 25 km resolution using satellite methane observations. We find a 13 % upward correction to the 2023 US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) for 2019, with large corrections to individual states, urban areas, and landfills. This may present a challenge for US climate policies and goals, many of which target significant reductions in methane emissions.
Emily Dowd, Alistair J. Manning, Bryn Orth-Lashley, Marianne Girard, James France, Rebecca E. Fisher, Dave Lowry, Mathias Lanoisellé, Joseph R. Pitt, Kieran M. Stanley, Simon O'Doherty, Dickon Young, Glen Thistlethwaite, Martyn P. Chipperfield, Emanuel Gloor, and Chris Wilson
Atmos. Meas. Tech., 17, 1599–1615, https://doi.org/10.5194/amt-17-1599-2024, https://doi.org/10.5194/amt-17-1599-2024, 2024
Short summary
Short summary
We provide the first validation of the satellite-derived emission estimates using surface-based mobile greenhouse gas surveys of an active gas leak detected near Cheltenham, UK. GHGSat’s emission estimates broadly agree with the surface-based mobile survey and steps were taken to fix the leak, highlighting the importance of satellite data in identifying emissions and helping to reduce our human impact on climate change.
Javier Roger, Luis Guanter, Javier Gorroño, and Itziar Irakulis-Loitxate
Atmos. Meas. Tech., 17, 1333–1346, https://doi.org/10.5194/amt-17-1333-2024, https://doi.org/10.5194/amt-17-1333-2024, 2024
Short summary
Short summary
Methane emissions can be identified using remote sensing, but surface-related structures disturb detection. In this work, a variation of the matched filter method that exploits a large fraction of the near-infrared range (1000–2500 nm) is applied. In comparison to the raw matched filter, it reduces background noise and strongly attenuates the surface-related artifacts, which leads to a greater detection capability. We propose this variation as a standard methodology for methane detection.
Jean-Philippe W. MacLean, Marianne Girard, Dylan Jervis, David Marshall, Jason McKeever, Antoine Ramier, Mathias Strupler, Ewan Tarrant, and David Young
Atmos. Meas. Tech., 17, 863–874, https://doi.org/10.5194/amt-17-863-2024, https://doi.org/10.5194/amt-17-863-2024, 2024
Short summary
Short summary
We demonstrate the capabilities of the GHGSat satellite constellation to detect and quantify offshore methane emissions using a sun glint observation mode. Using this technique, we observe offshore methane emissions from space ranging from 180 kg h−1 to 84 000 kg h−1. We further assess the instrument performance in offshore environments, both empirically and using analytical modelling, and find that the detection limit varies with latitude and season.
Alana K. Ayasse, Daniel Cusworth, Kelly O'Neill, Justin Fisk, Andrew K. Thorpe, and Riley Duren
Atmos. Meas. Tech., 16, 6065–6074, https://doi.org/10.5194/amt-16-6065-2023, https://doi.org/10.5194/amt-16-6065-2023, 2023
Short summary
Short summary
Methane is a powerful greenhouse gas, and a significant portion of methane comes from large individual plumes. Recently, airplane-mounted infrared technologies have proven very good at detecting and quantifying these plumes. In order to extract the methane signal from the infrared image, there are two widely used approaches. In this study, we assess the performance of both approaches using controlled-release experiments. We also examine the minimum detection limit of the infrared technology.
Daniel H. Cusworth, Andrew K. Thorpe, Charles E. Miller, Alana K. Ayasse, Ralph Jiorle, Riley M. Duren, Ray Nassar, Jon-Paul Mastrogiacomo, and Robert R. Nelson
Atmos. Chem. Phys., 23, 14577–14591, https://doi.org/10.5194/acp-23-14577-2023, https://doi.org/10.5194/acp-23-14577-2023, 2023
Short summary
Short summary
Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we tasked two satellites to routinely observe CO2 emissions at 30 coal-fired power plants between 2021 and 2022. These results present the largest dataset of space-based CO2 emission estimates to date.
Tim A. van Kempen, Tim J. Rotmans, Richard M. van Hees, Carol Bruegge, Dejian Fu, Ruud Hoogeveen, Thomas J. Pongetti, Robert Rosenberg, and Ilse Aben
Atmos. Meas. Tech., 16, 4507–4527, https://doi.org/10.5194/amt-16-4507-2023, https://doi.org/10.5194/amt-16-4507-2023, 2023
Short summary
Short summary
Validation of satellite measurements is essential for providing reliable and consistent products. In this paper, a validation method for TROPOMI-SWIR (Tropospheric Measurement Instrument in the short-wavelength infrared) is explored. TROPOMI-SWIR has been shown to be exceptionally stable, a necessity to explore the methodology. Railroad Valley, Nevada, is a prime location to perform the necessary measurements to validate the satellite measurements of TROPOMI-SWIR.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
Short summary
Short summary
A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Nicholas Balasus, Daniel J. Jacob, Alba Lorente, Joannes D. Maasakkers, Robert J. Parker, Hartmut Boesch, Zichong Chen, Makoto M. Kelp, Hannah Nesser, and Daniel J. Varon
Atmos. Meas. Tech., 16, 3787–3807, https://doi.org/10.5194/amt-16-3787-2023, https://doi.org/10.5194/amt-16-3787-2023, 2023
Short summary
Short summary
We use machine learning to remove biases in TROPOMI satellite observations of atmospheric methane, with GOSAT observations serving as a reference. We find that the TROPOMI biases relative to GOSAT are related to the presence of aerosols and clouds, the surface brightness, and the specific detector that makes the observation aboard TROPOMI. The resulting blended TROPOMI+GOSAT product is more reliable for quantifying methane emissions.
Gijs Leguijt, Joannes D. Maasakkers, Hugo A. C. Denier van der Gon, Arjo J. Segers, Tobias Borsdorff, and Ilse Aben
Atmos. Chem. Phys., 23, 8899–8919, https://doi.org/10.5194/acp-23-8899-2023, https://doi.org/10.5194/acp-23-8899-2023, 2023
Short summary
Short summary
We present a fast method to evaluate carbon monoxide emissions from cities in Africa. Carbon monoxide is important for climate change in an indirect way, as it is linked to ozone, methane, and carbon dioxide. Our measurements are made with a satellite that sees the entire globe every single day. This means that we can check from space whether the current knowledge of emission rates is up to date. We make the comparison and show that the emission rates in northern Africa are underestimated.
Ruosi Liang, Yuzhong Zhang, Wei Chen, Peixuan Zhang, Jingran Liu, Cuihong Chen, Huiqin Mao, Guofeng Shen, Zhen Qu, Zichong Chen, Minqiang Zhou, Pucai Wang, Robert J. Parker, Hartmut Boesch, Alba Lorente, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 8039–8057, https://doi.org/10.5194/acp-23-8039-2023, https://doi.org/10.5194/acp-23-8039-2023, 2023
Short summary
Short summary
We compare and evaluate East Asian methane emissions inferred from different satellite observations (GOSAT and TROPOMI). The results show discrepancies over northern India and eastern China. Independent ground-based observations are more consistent with TROPOMI-derived emissions in northern India and GOSAT-derived emissions in eastern China.
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023, https://doi.org/10.5194/acp-23-7503-2023, 2023
Short summary
Short summary
We use TROPOMI satellite observations to quantify weekly methane emissions from the US Permian oil and gas basin from May 2018 to October 2020. We find that Permian emissions are highly variable, with diverse economic and activity drivers. The most important drivers during our study period were new well development and natural gas price. Permian methane intensity averaged 4.6 % and decreased by 1 % per year.
Tobias Borsdorff, Teresa Campos, Natalie Kille, Kyle J. Zarzana, Rainer Volkamer, and Jochen Landgraf
Atmos. Meas. Tech., 16, 3027–3038, https://doi.org/10.5194/amt-16-3027-2023, https://doi.org/10.5194/amt-16-3027-2023, 2023
Short summary
Short summary
ECMWF plans to assimilate TROPOMI CO with their CAMS-IFS model. This will constrain the total column and the vertical CO distribution of the model. To show this, we combine individual TROPOMI CO column retrievals with different vertical sensitivities and obtain a vertical CO concentration profile. We test the approach on three CO pollution events in comparison with CAMS-IFS simulations that do not assimilate TROPOMI CO data and in situ airborne measurements of the BB-FLUX campaign.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
Short summary
Short summary
We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Anna Agustí-Panareda, Jérôme Barré, Sébastien Massart, Antje Inness, Ilse Aben, Melanie Ades, Bianca C. Baier, Gianpaolo Balsamo, Tobias Borsdorff, Nicolas Bousserez, Souhail Boussetta, Michael Buchwitz, Luca Cantarello, Cyril Crevoisier, Richard Engelen, Henk Eskes, Johannes Flemming, Sébastien Garrigues, Otto Hasekamp, Vincent Huijnen, Luke Jones, Zak Kipling, Bavo Langerock, Joe McNorton, Nicolas Meilhac, Stefan Noël, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Miha Razinger, Maximilian Reuter, Roberto Ribas, Martin Suttie, Colm Sweeney, Jérôme Tarniewicz, and Lianghai Wu
Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023, https://doi.org/10.5194/acp-23-3829-2023, 2023
Short summary
Short summary
We present a global dataset of atmospheric CO2 and CH4, the two most important human-made greenhouse gases, which covers almost 2 decades (2003–2020). It is produced by combining satellite data of CO2 and CH4 with a weather and air composition prediction model, and it has been carefully evaluated against independent observations to ensure validity and point out deficiencies to the user. This dataset can be used for scientific studies in the field of climate change and the global carbon cycle.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, and Jochen Landgraf
Atmos. Meas. Tech., 16, 1597–1608, https://doi.org/10.5194/amt-16-1597-2023, https://doi.org/10.5194/amt-16-1597-2023, 2023
Short summary
Short summary
In the TROPOMI methane data, there are few false methane anomalies that can be misinterpreted as enhancements caused by strong emission sources. These artefacts are caused by features of the underlying surfaces that are not well characterized in the retrieval algorithm. Here we improve the representation of the surface reflectance dependency with wavelength in the forward model, removing the artificial localized CH4 enhancements found in several locations like Siberia, Australia and Algeria.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
Short summary
Short summary
This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, and Jianxiong Sheng
Atmos. Chem. Phys., 23, 3325–3346, https://doi.org/10.5194/acp-23-3325-2023, https://doi.org/10.5194/acp-23-3325-2023, 2023
Short summary
Short summary
We combine satellite measurements with a novel downscaling method to map global methane emissions at 0.1°×0.1° resolution. These fine-scale emission estimates reveal unreported emission hotspots and shed light on the roles of agriculture, wetlands, and fossil fuels for regional methane budgets. The satellite-derived emissions point in particular to missing fossil fuel emissions in the Middle East and to a large emission underestimate in South Asia that appears to be tied to monsoon rainfall.
Adriana Bailey, Franziska Aemisegger, Leonie Villiger, Sebastian A. Los, Gilles Reverdin, Estefanía Quiñones Meléndez, Claudia Acquistapace, Dariusz B. Baranowski, Tobias Böck, Sandrine Bony, Tobias Bordsdorff, Derek Coffman, Simon P. de Szoeke, Christopher J. Diekmann, Marina Dütsch, Benjamin Ertl, Joseph Galewsky, Dean Henze, Przemyslaw Makuch, David Noone, Patricia K. Quinn, Michael Rösch, Andreas Schneider, Matthias Schneider, Sabrina Speich, Bjorn Stevens, and Elizabeth J. Thompson
Earth Syst. Sci. Data, 15, 465–495, https://doi.org/10.5194/essd-15-465-2023, https://doi.org/10.5194/essd-15-465-2023, 2023
Short summary
Short summary
One of the novel ways EUREC4A set out to investigate trade wind clouds and their coupling to the large-scale circulation was through an extensive network of isotopic measurements in water vapor, precipitation, and seawater. Samples were taken from the island of Barbados, from aboard two aircraft, and from aboard four ships. This paper describes the full collection of EUREC4A isotopic in situ data and guides readers to complementary remotely sensed water vapor isotope ratios.
Javier Gorroño, Daniel J. Varon, Itziar Irakulis-Loitxate, and Luis Guanter
Atmos. Meas. Tech., 16, 89–107, https://doi.org/10.5194/amt-16-89-2023, https://doi.org/10.5194/amt-16-89-2023, 2023
Short summary
Short summary
We present a methane flux rate retrieval methodology using the Sentinel-2 mission, validating the algorithm for different scenes and plumes. The detection limit is 1000–2000 kg h−1 for homogeneous scenes and temporally invariant surfaces and above 5000 kg h−1 for heterogeneous ones. Dominant quantification errors are wind-related or plume mask-related. For heterogeneous scenes, the surface structure underlying the methane plume can become a dominant source of uncertainty.
Srijana Lama, Sander Houweling, K. Folkert Boersma, Ilse Aben, Hugo A. C. Denier van der Gon, and Maarten C. Krol
Atmos. Chem. Phys., 22, 16053–16071, https://doi.org/10.5194/acp-22-16053-2022, https://doi.org/10.5194/acp-22-16053-2022, 2022
Short summary
Short summary
Hydroxyl radical (OH) is the important chemical species that determines the lifetime of some greenhouse gases and trace gases. OH plays a vital role in air pollution chemistry. OH has a short lifetime and is extremely difficult to measure directly. OH concentrations derived from the chemistry transport model (CTM) have uncertainties of >50 %. Therefore, in this study, OH is derived indirectly using satellite date in urban plumes.
Zhan Zhang, Evan D. Sherwin, Daniel J. Varon, and Adam R. Brandt
Atmos. Meas. Tech., 15, 7155–7169, https://doi.org/10.5194/amt-15-7155-2022, https://doi.org/10.5194/amt-15-7155-2022, 2022
Short summary
Short summary
This work developed a multi-band–multi-pass–multi-comparison-date Sentinel-2 methane retrieval algorithm, and the method was calibrated by data from a controlled release test. To our knowledge, this is the first study that validates the performance of a Sentinel-2 methane detection algorithm by calibration with a ground-truth testing. It illustrates the potential for additional validation with systematic future experiments wherein algorithms can be tuned to meet different detection expectations.
Alba Lorente, Tobias Borsdorff, Mari C. Martinez-Velarte, Andre Butz, Otto P. Hasekamp, Lianghai Wu, and Jochen Landgraf
Atmos. Meas. Tech., 15, 6585–6603, https://doi.org/10.5194/amt-15-6585-2022, https://doi.org/10.5194/amt-15-6585-2022, 2022
Short summary
Short summary
The TROPOspheric Monitoring Instrument (TROPOMI) performs observations over ocean in every orbit, enhancing the monitoring capabilities of methane from space. In the sun glint geometry the mirror-like reflection at the water surface provides a signal that is high enough to retrieve methane with high accuracy and precision. We present 4 years of methane concentrations over the ocean, and we assess its quality. We also show the importance of ocean observations to quantify total CH4 emissions.
Antje Inness, Ilse Aben, Melanie Ades, Tobias Borsdorff, Johannes Flemming, Luke Jones, Jochen Landgraf, Bavo Langerock, Philippe Nedelec, Mark Parrington, and Roberto Ribas
Atmos. Chem. Phys., 22, 14355–14376, https://doi.org/10.5194/acp-22-14355-2022, https://doi.org/10.5194/acp-22-14355-2022, 2022
Short summary
Short summary
The Copernicus Atmosphere Monitoring Service (CAMS) provides daily global air quality forecasts to users worldwide. One of the species of interest is carbon monoxide (CO), an important trace gas in the atmosphere with anthropogenic and natural sources, produced by incomplete combustion, for example, by wildfires. This paper looks at how well CAMS can model CO in the atmosphere and shows that the fields can be improved when blending CO data from the TROPOMI instrument with the CAMS model.
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, https://doi.org/10.5194/acp-22-11203-2022, 2022
Short summary
Short summary
We use 22 months of TROPOMI satellite observations to quantity methane emissions from the oil (O) and natural gas (G) sector in the US and Canada at the scale of both individual basins as well as country-wide aggregates. We find that O/G-related methane emissions are underestimated in these inventories by 80 % for the US and 40 % for Canada, and 70 % of the underestimate in the US is from five O/G basins, including Permian, Haynesville, Anadarko, Eagle Ford, and Barnett.
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, https://doi.org/10.5194/acp-22-10809-2022, 2022
Short summary
Short summary
We quantify methane emissions in China and contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We find that anthropogenic methane emissions for China are underestimated in the national inventory. Our estimate of emissions indicates a small life-cycle loss rate, implying net climate benefits from the current
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.
Sara Martínez-Alonso, Merritt N. Deeter, Bianca C. Baier, Kathryn McKain, Helen Worden, Tobias Borsdorff, Colm Sweeney, and Ilse Aben
Atmos. Meas. Tech., 15, 4751–4765, https://doi.org/10.5194/amt-15-4751-2022, https://doi.org/10.5194/amt-15-4751-2022, 2022
Short summary
Short summary
AirCore is a novel balloon sampling system that can measure, among others, vertical profiles of carbon monoxide (CO) from 25–30 km of altitude to near the surface. Our analyses of AirCore and satellite CO data show that AirCore profiles are suited for satellite data validation, the use of shorter aircraft vertical profiles in satellite validation results in small errors (1–3 percent points) mostly at 300 hPa and above, and the error introduced by clouds in TROPOMI land data is small (1–2 %).
Pieternel F. Levelt, Deborah C. Stein Zweers, Ilse Aben, Maite Bauwens, Tobias Borsdorff, Isabelle De Smedt, Henk J. Eskes, Christophe Lerot, Diego G. Loyola, Fabian Romahn, Trissevgeni Stavrakou, Nicolas Theys, Michel Van Roozendael, J. Pepijn Veefkind, and Tijl Verhoelst
Atmos. Chem. Phys., 22, 10319–10351, https://doi.org/10.5194/acp-22-10319-2022, https://doi.org/10.5194/acp-22-10319-2022, 2022
Short summary
Short summary
Using the COVID-19 lockdown periods as an example, we show how Sentinel-5P/TROPOMI trace gas data (NO2, SO2, CO, HCHO and CHOCHO) can be used to understand impacts on air quality for regions and cities around the globe. We also provide information for both experienced and inexperienced users about how we created the data using state-of-the-art algorithms, where to get the data, methods taking meteorological and seasonal variability into consideration, and insights for future studies.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
Short summary
Short summary
We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Daniel J. Jacob, Daniel J. Varon, Daniel H. Cusworth, Philip E. Dennison, Christian Frankenberg, Ritesh Gautam, Luis Guanter, John Kelley, Jason McKeever, Lesley E. Ott, Benjamin Poulter, Zhen Qu, Andrew K. Thorpe, John R. Worden, and Riley M. Duren
Atmos. Chem. Phys., 22, 9617–9646, https://doi.org/10.5194/acp-22-9617-2022, https://doi.org/10.5194/acp-22-9617-2022, 2022
Short summary
Short summary
We review the capability of satellite observations of atmospheric methane to quantify methane emissions on all scales. We cover retrieval methods, precision requirements, inverse methods for inferring emissions, source detection thresholds, and observations of system completeness. We show that current instruments already enable quantification of regional and national emissions including contributions from large point sources. Coverage and resolution will increase significantly in coming years.
Daniel J. Varon, Daniel J. Jacob, Melissa Sulprizio, Lucas A. Estrada, William B. Downs, Lu Shen, Sarah E. Hancock, Hannah Nesser, Zhen Qu, Elise Penn, Zichong Chen, Xiao Lu, Alba Lorente, Ashutosh Tewari, and Cynthia A. Randles
Geosci. Model Dev., 15, 5787–5805, https://doi.org/10.5194/gmd-15-5787-2022, https://doi.org/10.5194/gmd-15-5787-2022, 2022
Short summary
Short summary
Reducing atmospheric methane emissions is critical to slow near-term climate change. Globally surveying satellite instruments like the TROPOspheric Monitoring Instrument (TROPOMI) have unique capabilities for monitoring atmospheric methane around the world. Here we present a user-friendly cloud-computing tool that enables researchers and stakeholders to quantify methane emissions across user-selected regions of interest using TROPOMI satellite observations.
Jonas Hachmeister, Oliver Schneising, Michael Buchwitz, Alba Lorente, Tobias Borsdorff, John P. Burrows, Justus Notholt, and Matthias Buschmann
Atmos. Meas. Tech., 15, 4063–4074, https://doi.org/10.5194/amt-15-4063-2022, https://doi.org/10.5194/amt-15-4063-2022, 2022
Short summary
Short summary
Sentinel-5P trace gas retrievals rely on elevation data in their calculations. Outdated or inaccurate data can lead to significant errors in e.g. dry-air mole fractions of methane (XCH4). We show that the use of inadequate elevation data leads to strong XCH4 anomalies in Greenland. Similar problems can be expected for other regions with inaccurate elevation data. However, we expect these to be more localized. We show that updating elevation data used in the retrieval solves this issue.
Sudhanshu Pandey, Sander Houweling, and Arjo Segers
Geosci. Model Dev., 15, 4555–4567, https://doi.org/10.5194/gmd-15-4555-2022, https://doi.org/10.5194/gmd-15-4555-2022, 2022
Short summary
Short summary
Inversions are used to calculate methane emissions using atmospheric mole-fraction measurements. Multidecadal inversions are needed to extract information from the long measurement records of methane. However, multidecadal inversion computations can take months to finish. Here, we demonstrate an order of magnitude improvement in wall clock time for an iterative multidecadal inversion by physical parallelization of chemical transport model.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
Short summary
Short summary
This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
Short summary
Short summary
Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Andreas Schneider, Tobias Borsdorff, Joost aan de Brugh, Alba Lorente, Franziska Aemisegger, David Noone, Dean Henze, Rigel Kivi, and Jochen Landgraf
Atmos. Meas. Tech., 15, 2251–2275, https://doi.org/10.5194/amt-15-2251-2022, https://doi.org/10.5194/amt-15-2251-2022, 2022
Short summary
Short summary
This paper presents an extended H₂O/HDO total column dataset from short-wave infrared measurements by TROPOMI including cloudy and clear-sky scenes. Coverage is tremendously increased compared to previous TROPOMI HDO datasets. The new dataset is validated against recent ground-based FTIR measurements from TCCON and against aircraft measurements over the ocean. The use of the new dataset is demonstrated with a case study of a cold air outbreak in January 2020.
Elena Sánchez-García, Javier Gorroño, Itziar Irakulis-Loitxate, Daniel J. Varon, and Luis Guanter
Atmos. Meas. Tech., 15, 1657–1674, https://doi.org/10.5194/amt-15-1657-2022, https://doi.org/10.5194/amt-15-1657-2022, 2022
Short summary
Short summary
This study seeks to present the as-yet-unknown potential use of WorldView-3 for the mapping of methane point source emissions. The proposed retrieval methodology is based on the idea that the spectral channels not affected by methane can be used to predict the methane-affected band through regression analysis. The results show the precise location of 26 independent point emissions over different methane hotspot regions worldwide, which prove the game-changing potential that this mission entails.
Yousef Albuhaisi, Ype van der Velde, and Sander Houweling
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-55, https://doi.org/10.5194/bg-2022-55, 2022
Manuscript not accepted for further review
Short summary
Short summary
An important uncertainty in the modelling of methane emissions from natural wetlands is the wetland area. It is important to get the spatiotemporal covariance between the variables that drive methane emissions right for accurate quantification. Using high-resolution wetland and soil carbon maps, in combination with a simplified methane emission model that is coarsened in six steps from 0.005° to 1°, we find a strong relation between wetland emissions and the model resolution.
Tobias Küchler, Stefan Noël, Heinrich Bovensmann, John Philip Burrows, Thomas Wagner, Christian Borger, Tobias Borsdorff, and Andreas Schneider
Atmos. Meas. Tech., 15, 297–320, https://doi.org/10.5194/amt-15-297-2022, https://doi.org/10.5194/amt-15-297-2022, 2022
Short summary
Short summary
We applied the air-mass-corrected differential optical absorption spectroscopy (AMC-DOAS) method to derive total column water vapour (TCWV) from Sentinel-5P measurements and compared it to independent data sets. The correlation coefficients of typically more than 0.9 and the small deviations up to 2.5 kg m−2 reveal good agreement between our data product and other TCWV data sets. In particular for the different Sentinel-5P water vapour products, the deviations are around 1 kg m−2.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
Short summary
Short summary
We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Qiansi Tu, Frank Hase, Matthias Schneider, Omaira García, Thomas Blumenstock, Tobias Borsdorff, Matthias Frey, Farahnaz Khosrawi, Alba Lorente, Carlos Alberti, Juan J. Bustos, André Butz, Virgilio Carreño, Emilio Cuevas, Roger Curcoll, Christopher J. Diekmann, Darko Dubravica, Benjamin Ertl, Carme Estruch, Sergio Fabián León-Luis, Carlos Marrero, Josep-Anton Morgui, Ramón Ramos, Christian Scharun, Carsten Schneider, Eliezer Sepúlveda, Carlos Toledano, and Carlos Torres
Atmos. Chem. Phys., 22, 295–317, https://doi.org/10.5194/acp-22-295-2022, https://doi.org/10.5194/acp-22-295-2022, 2022
Short summary
Short summary
We use different methane ground- and space-based remote sensing data sets for investigating the emission strength of three waste disposal sites close to Madrid. We present a method that uses wind-assigned anomalies for deriving emission strengths from satellite data and estimate their uncertainty to 9–14 %. The emission strengths estimated from the remote sensing data sets are significantly larger than the values published in the official register.
Sabour Baray, Daniel J. Jacob, Joannes D. Maasakkers, Jian-Xiong Sheng, Melissa P. Sulprizio, Dylan B. A. Jones, A. Anthony Bloom, and Robert McLaren
Atmos. Chem. Phys., 21, 18101–18121, https://doi.org/10.5194/acp-21-18101-2021, https://doi.org/10.5194/acp-21-18101-2021, 2021
Short summary
Short summary
We use 2010–2015 surface and satellite observations to disentangle methane from anthropogenic and natural sources in Canada. Using a chemical transport model (GEOS-Chem), the mismatch between modelled and observed methane concentrations can be used to infer emissions according to Bayesian statistics. Compared to prior knowledge, we show higher anthropogenic emissions attributed to energy and/or agriculture in Western Canada and lower natural emissions from Boreal wetlands.
Tyler Wizenberg, Kimberly Strong, Kaley Walker, Erik Lutsch, Tobias Borsdorff, and Jochen Landgraf
Atmos. Meas. Tech., 14, 7707–7728, https://doi.org/10.5194/amt-14-7707-2021, https://doi.org/10.5194/amt-14-7707-2021, 2021
Short summary
Short summary
CO is an important atmospheric gas that influences both air quality and the climate. Here, we compare CO measurements from TROPOMI with those from ACE-FTS and an Arctic ground-based FTS at Eureka, Nunavut, to further characterize the accuracy of TROPOMI measurements. CO columns from the instruments agree well but show larger differences at high latitudes. Despite this, the results fall within the TROPOMI accuracy target, indicating good data quality at high latitudes.
Luis Guanter, Cédric Bacour, Andreas Schneider, Ilse Aben, Tim A. van Kempen, Fabienne Maignan, Christian Retscher, Philipp Köhler, Christian Frankenberg, Joanna Joiner, and Yongguang Zhang
Earth Syst. Sci. Data, 13, 5423–5440, https://doi.org/10.5194/essd-13-5423-2021, https://doi.org/10.5194/essd-13-5423-2021, 2021
Short summary
Short summary
Sun-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by plants in the red and far-red parts of the spectrum. It has a functional link to photosynthesis and can be measured by satellite instruments, which makes it an important variable for the remote monitoring of the photosynthetic activity of vegetation ecosystems around the world. In this contribution we present a SIF dataset derived from the new Sentinel-5P TROPOMI missions.
Vilma Kangasaho, Aki Tsuruta, Leif Backman, Pyry Mäkinen, Sander Houweling, Arjo Segers, Maarten Krol, Ed Dlugokencky, Sylvia Michel, James White, and Tuula Aalto
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-843, https://doi.org/10.5194/acp-2021-843, 2021
Revised manuscript not accepted
Short summary
Short summary
Understanding the composition of carbon isotopes can help to better understand the changes in methane budgets. This study investigates how methane sources affect the seasonal cycle of the methane carbon-13 isotope during 2000–2012 using an atmospheric transport model. We found that emissions from both anthropogenic and natural sources contribute. The findings raise a need to revise the magnitudes, proportion, and seasonal cycles of anthropogenic sources and northern wetland emissions.
Mahesh Kumar Sha, Bavo Langerock, Jean-François L. Blavier, Thomas Blumenstock, Tobias Borsdorff, Matthias Buschmann, Angelika Dehn, Martine De Mazière, Nicholas M. Deutscher, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Michel Grutter, James W. Hannigan, Frank Hase, Pauli Heikkinen, Christian Hermans, Laura T. Iraci, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Nicolas Kumps, Jochen Landgraf, Alba Lorente, Emmanuel Mahieu, Maria V. Makarova, Johan Mellqvist, Jean-Marc Metzger, Isamu Morino, Tomoo Nagahama, Justus Notholt, Hirofumi Ohyama, Ivan Ortega, Mathias Palm, Christof Petri, David F. Pollard, Markus Rettinger, John Robinson, Sébastien Roche, Coleen M. Roehl, Amelie N. Röhling, Constantina Rousogenous, Matthias Schneider, Kei Shiomi, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Osamu Uchino, Voltaire A. Velazco, Corinne Vigouroux, Mihalis Vrekoussis, Pucai Wang, Thorsten Warneke, Tyler Wizenberg, Debra Wunch, Shoma Yamanouchi, Yang Yang, and Minqiang Zhou
Atmos. Meas. Tech., 14, 6249–6304, https://doi.org/10.5194/amt-14-6249-2021, https://doi.org/10.5194/amt-14-6249-2021, 2021
Short summary
Short summary
This paper presents, for the first time, Sentinel-5 Precursor methane and carbon monoxide validation results covering a period from November 2017 to September 2020. For this study, we used global TCCON and NDACC-IRWG network data covering a wide range of atmospheric and surface conditions across different terrains. We also show the influence of a priori alignment, smoothing uncertainties and the sensitivity of the validation results towards the application of advanced co-location criteria.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
Short summary
Short summary
The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
Short summary
Short summary
We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Tia R. Scarpelli, Melissa P. Sulprizio, Yuzhong Zhang, and Chris H. Rycroft
Atmos. Meas. Tech., 14, 5521–5534, https://doi.org/10.5194/amt-14-5521-2021, https://doi.org/10.5194/amt-14-5521-2021, 2021
Short summary
Short summary
Analytical inversions of satellite observations of atmospheric composition can improve emissions estimates and quantify errors but are computationally expensive at high resolutions. We propose two methods to decrease this cost. The methods reproduce a high-resolution inversion at a quarter of the cost. The reduced-dimension method creates a multiscale grid. The reduced-rank method solves the inversion where information content is highest.
David R. Lyon, Benjamin Hmiel, Ritesh Gautam, Mark Omara, Katherine A. Roberts, Zachary R. Barkley, Kenneth J. Davis, Natasha L. Miles, Vanessa C. Monteiro, Scott J. Richardson, Stephen Conley, Mackenzie L. Smith, Daniel J. Jacob, Lu Shen, Daniel J. Varon, Aijun Deng, Xander Rudelis, Nikhil Sharma, Kyle T. Story, Adam R. Brandt, Mary Kang, Eric A. Kort, Anthony J. Marchese, and Steven P. Hamburg
Atmos. Chem. Phys., 21, 6605–6626, https://doi.org/10.5194/acp-21-6605-2021, https://doi.org/10.5194/acp-21-6605-2021, 2021
Short summary
Short summary
The Permian Basin (USA) is the world’s largest oil field. We use tower- and aircraft-based approaches to measure how methane emissions in the Permian Basin changed throughout 2020. In early 2020, 3.3 % of the region’s gas was emitted; then in spring 2020, the loss rate temporarily dropped to 1.9 % as oil price crashed. We find this short-term reduction to be a result of reduced well development, less gas flaring, and fewer abnormal events despite minimal reductions in oil and gas production.
Daniel J. Varon, Dylan Jervis, Jason McKeever, Ian Spence, David Gains, and Daniel J. Jacob
Atmos. Meas. Tech., 14, 2771–2785, https://doi.org/10.5194/amt-14-2771-2021, https://doi.org/10.5194/amt-14-2771-2021, 2021
Short summary
Short summary
Satellites can detect methane emissions by measuring sunlight reflected from the Earth's surface and atmosphere. Here we show that the European Space Agency's Sentinel-2 twin satellites can be used to monitor anomalously large methane point sources around the world, with global coverage every 2–5 days and 20 m spatial resolution. We demonstrate this previously unreported capability through high-frequency Sentinel-2 monitoring of two strong methane point sources in Algeria and Turkmenistan.
Jérôme Barré, Ilse Aben, Anna Agustí-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Peter Dueben, Richard Engelen, Antje Inness, Alba Lorente, Joe McNorton, Vincent-Henri Peuch, Gabor Radnoti, and Roberto Ribas
Atmos. Chem. Phys., 21, 5117–5136, https://doi.org/10.5194/acp-21-5117-2021, https://doi.org/10.5194/acp-21-5117-2021, 2021
Short summary
Short summary
This study presents a new approach to the systematic global detection of anomalous local CH4 concentration anomalies caused by rapid changes in anthropogenic emission levels. The approach utilises both satellite measurements and model simulations, and applies novel data analysis techniques (such as filtering and classification) to automatically detect anomalous emissions from point sources and small areas, such as oil and gas drilling sites, pipelines and facility leaks.
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, and Shuang Ma
Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, https://doi.org/10.5194/acp-21-4637-2021, 2021
Short summary
Short summary
We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jianxiong Sheng, Yuzhong Zhang, Xiao Lu, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, and Robert J. Parker
Atmos. Chem. Phys., 21, 4339–4356, https://doi.org/10.5194/acp-21-4339-2021, https://doi.org/10.5194/acp-21-4339-2021, 2021
Short summary
Short summary
We use 2010–2015 GOSAT satellite observations of atmospheric methane over North America in a high-resolution inversion to estimate methane emissions. We find general consistency with the gridded EPA inventory but higher oil and gas production emissions, with oil production emissions twice as large as in the latest EPA Greenhouse Gas Inventory. We find lower wetland emissions than predicted by WetCHARTs and a small increasing trend in the eastern US, apparently related to unconventional oil/gas.
Dylan Jervis, Jason McKeever, Berke O. A. Durak, James J. Sloan, David Gains, Daniel J. Varon, Antoine Ramier, Mathias Strupler, and Ewan Tarrant
Atmos. Meas. Tech., 14, 2127–2140, https://doi.org/10.5194/amt-14-2127-2021, https://doi.org/10.5194/amt-14-2127-2021, 2021
Short summary
Short summary
We describe how the GHGSat-D demonstration satellite is designed and operated in order to measure greenhouse gas emissions from different types of industrial facilities. The distinguishing features of GHGSat-D, or
Claire, are its compact size (< 15 kg) and high spatial resolution (< 50 m). We give a mathematical model of the instrument and describe the techniques used to infer a methane concentration from a measurement of the sunlight that has reflected off the Earth's surface.
Michael Buchwitz, Maximilian Reuter, Stefan Noël, Klaus Bramstedt, Oliver Schneising, Michael Hilker, Blanca Fuentes Andrade, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Hartmut Boesch, Lianghai Wu, Jochen Landgraf, Ilse Aben, Christian Retscher, Christopher W. O'Dell, and David Crisp
Atmos. Meas. Tech., 14, 2141–2166, https://doi.org/10.5194/amt-14-2141-2021, https://doi.org/10.5194/amt-14-2141-2021, 2021
Short summary
Short summary
The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. We have used a small ensemble of satellite retrievals of column-averaged CO2 (XCO2) to find out if a regional-scale reduction of atmospheric CO2 can be detected from space. We focus on East China and show that it is challenging to reliably detect and to accurately quantify the emission reduction, which only results in regional XCO2 reductions of about 0.1–0.2 ppm.
Qiansi Tu, Frank Hase, Thomas Blumenstock, Matthias Schneider, Andreas Schneider, Rigel Kivi, Pauli Heikkinen, Benjamin Ertl, Christopher Diekmann, Farahnaz Khosrawi, Michael Sommer, Tobias Borsdorff, and Uwe Raffalski
Atmos. Meas. Tech., 14, 1993–2011, https://doi.org/10.5194/amt-14-1993-2021, https://doi.org/10.5194/amt-14-1993-2021, 2021
Short summary
Short summary
We compare column-averaged dry-air mole fractions of water vapor (XH2O) retrievals from the COllaborative Carbon Column Observing Network (COCCON) with two co-located ground-based spectrometers as references at two boreal sites. Our study supports the assumption that COCCON also delivers a well-characterized XH2O data product. This is the first published study applying COCCON for MUSICA IASI and TROPOMI validation.
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, https://doi.org/10.5194/acp-21-3643-2021, 2021
Short summary
Short summary
We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical and boreal wetlands and tropical livestock (South Asia, Africa, Brazil), with an insignificant positive trend in emissions from the fossil fuel sector. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
Marvin Knapp, Ralph Kleinschek, Frank Hase, Anna Agustí-Panareda, Antje Inness, Jérôme Barré, Jochen Landgraf, Tobias Borsdorff, Stefan Kinne, and André Butz
Earth Syst. Sci. Data, 13, 199–211, https://doi.org/10.5194/essd-13-199-2021, https://doi.org/10.5194/essd-13-199-2021, 2021
Short summary
Short summary
We developed a shipborne variant of a remote sensing spectrometer for direct sunlight measurements of column-averaged atmospheric mixing ratios of carbon dioxide, methane, and carbon monoxide. The instrument was deployed on the research vessel Sonne during a longitudinal transect over the Pacific during June 2019. The campaign yielded more than 32 000 observations which compare excellently to atmospheric composition data from a state-of-the-art model (CAMS) and satellite observations (TROPOMI).
Alba Lorente, Tobias Borsdorff, Andre Butz, Otto Hasekamp, Joost aan de Brugh, Andreas Schneider, Lianghai Wu, Frank Hase, Rigel Kivi, Debra Wunch, David F. Pollard, Kei Shiomi, Nicholas M. Deutscher, Voltaire A. Velazco, Coleen M. Roehl, Paul O. Wennberg, Thorsten Warneke, and Jochen Landgraf
Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, https://doi.org/10.5194/amt-14-665-2021, 2021
Short summary
Short summary
TROPOMI aboard Sentinel-5P satellite provides methane (CH4) measurements with exceptional temporal and spatial resolution. The study describes a series of improvements developed to retrieve CH4 from TROPOMI. The updated CH4 product features (among others) a more accurate a posteriori correction derived independently of any reference data. The validation of the improved data product shows good agreement with ground-based and satellite measurements, which highlights the quality of the TROPOMI CH4.
Sudhanshu Pandey, Sander Houweling, Alba Lorente, Tobias Borsdorff, Maria Tsivlidou, A. Anthony Bloom, Benjamin Poulter, Zhen Zhang, and Ilse Aben
Biogeosciences, 18, 557–572, https://doi.org/10.5194/bg-18-557-2021, https://doi.org/10.5194/bg-18-557-2021, 2021
Short summary
Short summary
We use atmospheric methane observations from the novel TROPOspheric Monitoring Instrument (TROPOMI; Sentinel-5p) to estimate methane emissions from South Sudan's wetlands. Our emission estimates are an order of magnitude larger than the estimate of process-based wetland models. We find that this underestimation by the models is likely due to their misrepresentation of the wetlands' inundation extent and temperature dependences.
Ivar R. van der Velde, Guido R. van der Werf, Sander Houweling, Henk J. Eskes, J. Pepijn Veefkind, Tobias Borsdorff, and Ilse Aben
Atmos. Chem. Phys., 21, 597–616, https://doi.org/10.5194/acp-21-597-2021, https://doi.org/10.5194/acp-21-597-2021, 2021
Short summary
Short summary
This paper compares the relative atmospheric enhancements of CO and NO2 measured by the space-based instrument TROPOMI over different fire-prone ecosystems around the world. We find distinct spatial and temporal patterns in the ΔNO2 / ΔCO ratio that correspond to regional differences in combustion efficiency. This joint analysis provides a better understanding of regional-scale combustion characteristics and can help the fire modeling community to improve existing global emission inventories.
Tobias Borsdorff, Agustín García Reynoso, Gilberto Maldonado, Bertha Mar-Morales, Wolfgang Stremme, Michel Grutter, and Jochen Landgraf
Atmos. Chem. Phys., 20, 15761–15774, https://doi.org/10.5194/acp-20-15761-2020, https://doi.org/10.5194/acp-20-15761-2020, 2020
Cited articles
ASI – Agenzia Spaziale Italiana (Italian Space Agency): The PRISMA data portal, https://prismauserregistration.asi.it (last access: 20 April, 2023), 2023. a
Bloom, A., Bowman, K., Lee, M., Turner, A., Schroeder, R., Worden, J., Weidner, R., McDonald, K., and Jacob, D.: CMS: Global 0.5-deg Wetland Methane
Emissions and Uncertainty (WetCHARTs v1.3.1), ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1915, 2021. a, b
Borsdorff, T., Aan De Brugh, J., Hu, H., Hasekamp, O., Sussmann, R., Rettinger, M., Hase, F., Gross, J., Schneider, M., Garcia, O., Stremme, W., Grutter, M., Feist, D. G., Arnold, S. G., De Mazière, M., Kumar Sha, M., Pollard, D. F., Kiel, M., Roehl, C., Wennberg, P. O., Toon, G. C., and Landgraf, J.: Mapping carbon monoxide pollution from space down to city scales with daily global coverage, Atmos. Meas. Tech., 11, 5507–5518,
https://doi.org/10.5194/amt-11-5507-2018, 2018. a
Breiman, L.: Random Forests, Mach. Learn. 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Chen, Z., Jacob, D. J., Nesser, H., Sulprizio, M. P., Lorente, A., Varon,
D. J., Lu, X., Shen, L., Qu, Z., Penn, E., and Yu, X.: Methane emissions
from China: a high-resolution inversion of TROPOMI satellite observations,
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022, 2022. a, b
Cheng, G., Xie, X., Han, J., Guo, L., and Xia, G. S.: Remote Sensing Image
Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks,
and Opportunities, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 13, 3735–3756, https://doi.org/10.1109/JSTARS.2020.3005403, 2020. a, b, c, d
Chollet, F.: Deep Learning with Python, Manning, ISBN 13:978-1617296864, 2021. a
Chollet, F., et al.: Keras, https://keras.io (last access: 20 April 2023), 2015. a
Cogliati, S., Sarti, F., Chiarantini, L., Cosi, M., Lorusso, R., Lopinto, E.,
Miglietta, F., Genesio, L., Guanter, L., Damm, A., Pérez-López,
S., Scheffler, D., Tagliabue, G., Panigada, C., Rascher, U., Dowling, T. P.,
Giardino, C., and Colombo, R.: The PRISMA imaging spectroscopy mission:
overview and first performance analysis, Remote Sens. Environ., 262,
112499, https://doi.org/10.1016/j.rse.2021.112499, 2021. a, b, c
Cohen, J.: A Coefficient of Agreement for Nominal Scales, Educat. Psychol. Meas., 20, 37–46, https://doi.org/10.1177/001316446002000104, 1960. a
Contributors to the WRF repository: The official repository for the Weather Research and Forecasting (WRF) model, GitHub [code], https://github.com/wrf-model/WRF/releases/ (last access: 20 April 2023), 2023. a
Copernicus Climate Change Service: Climate Data Store,
https://cds.climate.copernicus.eu/ (last access: 20 April 2023), 2023. a
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo, E., Monforti-Ferrario, F., Olivier, J., and Vignati, E.: EDGAR v6.0 Greenhouse Gas Emissions [Dataset], European Commission, Joint Research Centre (JRC) [data set], http://data.europa.eu/89h/97a67d67-c62e-4826-b873-9d972c4f670b (last access: 20 April 2023), 2021. a, b
Cusworth, D. H., Jacob, D. J., Varon, D. J., Chan Miller, C., Liu, X., Chance, K., Thorpe, A. K., Duren, R. M., Miller, C. E., Thompson, D. R., Frankenberg, C., Guanter, L., and Randles, C. A.: Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space, Atmos. Meas. Tech., 12, 5655–5668, https://doi.org/10.5194/amt-12-5655-2019, 2019. a
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Tseng, E., Thompson, D., Guha,
A., Newman, S., Foster, K. T., and Miller, C. E.: Using remote sensing to
detect, validate, and quantify methane emissions from California solid waste
operations, Environ. Res. Lett., 15, 054012, https://doi.org/10.1088/1748-9326/ab7b99, 2020. a
Cusworth, D. H., Duren, R. M., Thorpe, A. K., Pandey, S., Maasakkers, J. D.,
Aben, I., Jervis, D., Varon, D. J., Jacob, D. J., Randles, C. A., Gautam, R.,
Omara, M., Schade, G. W., Dennison, P. E., Frankenberg, C., Gordon, D.,
Lopinto, E., and Miller, C. E.: Multisatellite Imaging of a Gas Well Blowout
Enables Quantification of Total Methane Emissions, Geophys. Res. Lett., 48, e2020GL090864, https://doi.org/10.1029/2020GL090864, 2021. a, b, c, d
de Gouw, J. A., Veefkind, J. P., Roosenbrand, E., Dix, B., Lin, J. C.,
Landgraf, J., and Levelt, P. F.: Daily Satellite Observations of Methane
from Oil and Gas Production Regions in the United States, Sci. Rep., 10, 1–10, https://doi.org/10.1038/s41598-020-57678-4, 2020. a
Dekker, I. N., Houweling, S., Aben, I., Röckmann, T., Krol, M.,
Martínez-Alonso, S., Deeter, M. N., and Worden, H. M.: Quantification of
CO emissions from the city of Madrid using MOPITT satellite retrievals and
WRF simulations, Atmos. Chem. Phys., 17, 14675–14694,
https://doi.org/10.5194/acp-17-14675-2017, 2017. a
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F.,
Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F.,
Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA's Optical
High-Resolution Mission for GMES Operational Services, Remote Sens. Environ., 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026, 2012. a, b
Duren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M., Yadav,
V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K., Frankenberg, C.,
McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D., Croes, B. E.,
Green, R. O., and Miller, C. E.: California's methane super-emitters,
Nature, 575, 180–184, https://doi.org/10.1038/s41586-019-1720-3, 2019. a
ESA: GHGSat: GHGSat mission overview, ESA,
https://earth.esa.int/eogateway/missions/ghgsat (last access: 20 April 2023), 2022. a
ESA: Copernicus Open Access Hub, https://scihub.copernicus.eu/,
(last access: 20 April 2023), 2023. a
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, 226–231, https://aaai.org/ (last access: 20 April 2023), 1996. a
European Commission: Launch by United States, the European Union, and
Partners of the Global Methane Pledge to Keep 1.5 ∘C Within Reach,
https://ec.europa.eu/commission/presscorner/detail/en/statement_21_5766
(last access: 20 April 2023), 2021. a
Finch, D. P., Palmer, P. I., and Zhang, T.: Automated detection of atmospheric NO2 plumes from satellite data: A tool to help infer anthropogenic combustion emissions, Atmos. Meas. Tech., 15, 721–733,
https://doi.org/10.5194/amt-15-721-2022, 2022. a
Frankenberg, C., Thorpe, A. K., Thompson, D. R., Hulley, G., Kort, E. A.,
Vance, N., Borchardt, J., Krings, T., Gerilowski, K., Sweeney, C., Conley,
S., Bue, B. D., Aubrey, A. D., Hook, S., and Green, R. O.: Airborne methane
remote measurements reveal heavytail flux distribution in Four Corners
region, P. Natl. Acad. Sci. USA, 113, 9734–9739, https://doi.org/10.1073/pnas.1605617113, 2016. a, b, c
GMAO, The Global Modeling and Assimilation Office, and NASA: GMAO data products, https://gmao.gsfc.nasa.gov/GMAO_products/ (last access: 20 April 2023), 2023. a
Gorroño, J., Varon, D. J., Irakulis-Loitxate, I., and Guanter, L.: Understanding the potential of Sentinel-2 for monitoring methane point emissions, Atmos. Meas. Tech., 16, 89–107, https://doi.org/10.5194/amt-16-89-2023, 2023. a, b, c
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975,
https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Guanter, L., Irakulis-Loitxate, I., Gorroño, J.,
Sánchez-García, E., Cusworth, D. H., Varon, D. J., Cogliati, S.,
and Colombo, R.: Mapping methane point emissions with the PRISMA spaceborne
imaging spectrometer, Remote Sens. Environ., 265, 112671,
https://doi.org/10.1016/j.rse.2021.112671, 2021. a, b, c, d
Hasekamp, O., Lorente, A., Hu, H., Butz, A., Aan de Brugh, J., and Landgraf,
J.: Algorithm Theoretical Baseline Document for Sentinel-5 Precursor methane
Retrieval, SRON The Netherlands Institute for Space Research, Leiden, the
Netherlands,
https://sentinels.copernicus.eu/documents/247904/2476257/Sentinel-5P-TROPOMI-ATBD-Methane-retrieval.pdf/f275eb1d-89a8-464f-b5b8-c7156cda874e?t=1658313508597
(last access: 20 April 2023), 2022. a, b
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image
Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Los Alamitos, CA, USA, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5
global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hu, H., Landgraf, J., Detmers, R., Borsdorff, T., Aan de Brugh, J., Aben, I.,
Butz, A., and Hasekamp, O.: Toward Global Mapping of Methane With TROPOMI:
First Results and Intersatellite Comparison to GOSAT, Geophy. Res. Lett., 45, 3682–3689, https://doi.org/10.1002/2018GL077259, 2018. a
IPCC: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021. a, b
Iqbal, H.: HarisIqbal88/PlotNeuralNet v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.2526396, 2018. a
Irakulis-Loitxate, I., Gorroño, J., Zavala-Araiza, D., and Guanter, L.:
Satellites Detect a Methane Ultra-emission Event from an Offshore Platform in
the Gulf of Mexico, Environ. Sci. Technol. Lett., 9, 520–525,
https://doi.org/10.1021/acs.estlett.2c00225, 2022a. a
Irakulis-Loitxate, I., Guanter, L., Maasakkers, J. D., Zavala-Araiza, D., and
Aben, I.: Satellites Detect Abatable Super-Emissions in One of the World’s
Largest Methane Hotspot Regions, Environ. Sci. Technol., 56, 2143–2152, https://doi.org/10.1021/acs.est.1c04873, 2022b. a, b, c, d
Jacob, D. J., Turner, A. J., Maasakkers, J. D., Sheng, J., Sun, K., Liu, X.,
Chance, K., Aben, I., McKeever, J., and Frankenberg, C.: Satellite
observations of atmospheric methane and their value for quantifying methane
emissions, Atmos. Chem. Phys, 16, 14371–14396, https://doi.org/10.5194/acp-16-14371-2016, 2016. a, b, c
Jacob, D. J., Varon, D. J., Cusworth, D. H., Dennison, P. E., Frankenberg, C., Gautam, R., Guanter, L., Kelley, J., McKeever, J., Ott, L. E., Poulter, B., Qu, Z., Thorpe, A. K., Worden, J. R., and Duren, R. M.: Quantifying methane emissions from the global scale down to point sources using satellite
observations of atmospheric methane, Atmos. Chem. Phys., 22,
9617–9646, https://doi.org/10.5194/acp-22-9617-2022, 2022. a, b, c
Jervis, D., McKeever, J., Durak, B. O., Sloan, J. J., Gains, D., Varon, D. J., Ramier, A., Strupler, M., and Tarrant, E.: The GHGSat-D imaging
spectrometer, Atmos. Meas. Tech., 14, 2127–2140,
https://doi.org/10.5194/amt-14-2127-2021, 2021. a, b
Johnson, J. M. and Khoshgoftaar, T. M.: Survey on deep learning with class
imbalance, J. Big Data, 6, 27, https://doi.org/10.1186/s40537-019-0192-5, 2019. a, b, c
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization,
arxiv [preprint], http://arxiv.org/abs/1412.6980 (last access: 20 April 2023), 2014. a
Lauvaux, T., Giron, C., Mazzolini, M., D'aspremont, A., Duren, R., and
Cusworth, D.: Global assessment of oil and gas methane ultra-emitters,
Science, 375, 557–561, https://doi.org/10.1126/science.abj4351, 2022. a, b, c, d
Li, L., Jamieson, K., Rostamizadeh, A., and Talwalkar, A.: Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization, J. Mach. Learn. Res., 18, 1–52, 2018. a
Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., Aan De Brugh, J.,
Schneider, A., Wu, L., Hase, F., Kivi, R., Wunch, D., Pollard, D. F., Shiomi,
K., Deutscher, N. M., Velazco, V. A., Roehl, C. M., Wennberg, P. O., Warneke,
T., and Landgraf, J.: Methane retrieved from TROPOMI: Improvement of the
data product and validation of the first 2 years of measurements, Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, 2021. a, b, c
Lorente, A., Borsdorff, T., Landgraf, J., and SRON L2 team: TROPOMI scientific
Maasakkers, J. D., Omara, M., Gautam, R., Lorente, A., Pandey, S., Tol, P.,
Borsdorff, T., Houweling, S., and Aben, I.: Reconstructing and quantifying
methane emissions from the full duration of a 38-day natural gas well blowout
using space-based observations, Remote Sens. Environ., 270, 112755, https://doi.org/10.1016/j.rse.2021.112755, 2022a. a, b, c
Maasakkers, J. D., Varon, D. J., Elfarsdóttir, A., McKeever, J., Jervis, D., Mahapatra, G., Pandey, S., Lorente, A., Borsdorff, T., Foorthuis, L. R.,
Schuit, B. J., Tol, P., van Kempen, T. A., van Hees, R., and Aben, I.: Using
satellites to uncover large methane emissions from landfills, Sci. Adv, 8,
9683, https://doi.org/10.1126/sciadv.abn9683, 2022b. a, b, c, d, e, f, g, h, i, j
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and Eichmann,
A.: The GEOS-5 Atmospheric General Circulation Model: Mean Climate and
Development from MERRA to Fortuna, Technical Report Series on Global Modeling
and Data Assimilation, Tech. Rep., https://ntrs.nasa.gov/citations/20120011790 (last access: 25 July 2023), 2012. a, b, c, d, e, f, g, h, i, j
Nisbet, E. G., Fisher, R. E., Lowry, D., France, J. L., Allen, G., Bakkaloglu, S., Broderick, T. J., Cain, M., Coleman, M., Fernandez, J., Forster, G., Griffiths, P. T., Iverach, C. P., Kelly, B. F., Manning, M. R., Nisbet-Jones, P. B., Pyle, J. A., Townsend-Small, A., al Shalaan, A., Warwick, N., and Zazzeri, G.: Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement, Rev. Geophys., 58, e2019RG000675, https://doi.org/10.1029/2019RG000675, 2020. a, b, c
NOAA: Trends in Atmospheric Methane, NOAA,
https://gml.noaa.gov/ccgg/trends_ch4/ (last access: 20 April 2023), 2022. a
Ocko, I. B., Naik, V., and Paynter, D.: Rapid and reliable assessment of
methane impacts on climate, Atmos. Chem. Phys., 18, 15555–15568, https://doi.org/10.5194/acp-18-15555-2018, 2018. a
Ocko, I. B., Sun, T., Shindell, D., Oppenheimer, M., Hristov, A. N., Pacala,
S. W., Mauzerall, D. L., Xu, Y., and Hamburg, S. P.: Acting rapidly to
deploy readily available methane mitigation measures by sector can
immediately slow global warming, Environ. Res. Lett., 16, 054042, https://doi.org/10.1088/1748-9326/abf9c8, 2021. a, b
O'Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L.,
et al.: KerasTuner, GitHub [code], https://github.com/keras-team/keras-tuner (last access: 20 April 2023), 2019. a
Pandey, S., Gautam, R., Houweling, S., Denier Van Der Gon, H., Sadavarte, P.,
Borsdorff, T., Hasekamp, O., Landgraf, J., Tol, P., Van Kempen, T.,
Hoogeveen, R., Van Hees, R., Hamburg, S. P., Maasakkers, J. D., and Aben, I.:
Satellite observations reveal extreme methane leakage from a natural gas
well blowout, P. Natl. Acad. Sci. USA, 116, 26376–26381, https://doi.org/10.1073/pnas.1908712116, 2019. a, b
Pandey, S., Houweling, S., Lorente, A., Borsdorff, T., Tsivlidou, M.,
Anthony Bloom, A., Poulter, B., Zhang, Z., and Aben, I.: Using satellite
data to identify the methane emission controls of South Sudan's wetlands,
Biogeosciences, 18, 557–572, https://doi.org/10.5194/bg-18-557-2021, 2021. a, b
Parry, I., Black, S., Minnett, D., Mylonas, V., and Vernon, N.: How to Cut
Methane Emissions; IMF Staff Climate Note 2022/008, October 2022, Tech. rep., International Monetary Fund, https://www.imf.org/en/Publications/staff-climate-notes/Issues/2022/10/28/How-to-Cut-Methane-Emissions-525188 (last access: 20 April 2023), 2022. a
Plant, G., Kort, E. A., Brandt, A. R., Chen, Y., Fordice, G., Gorchov Negron,
A. M., Schwietzke, S., Smith, M., and Zavala-Araiza, D.: Inefficient and
unlit natural gas flares both emit large quantities of methane, Science,
377, 1566–1571, https://doi.org/10.1126/science.abq0385, 2022. a, b
Qu, Z., Jacob, D. J., Shen, L., Lu, X., Zhang, Y., Scarpelli, T. R., Nesser,
H., Sulprizio, M. P., Maasakkers, J. D., Bloom, A. A., Worden, J. R., Parker,
R. J., and Delgado, A. L.: Global distribution of methane emissions: A
comparative inverse analysis of observations from the TROPOMI and GOSAT
satellite instruments, Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, 2021. a
Ramier, A., Deglint, H., Gains, D., Jervis, D., McKeever, J., Shaw, W.,
Strupler, M., Tarrant, E., and Varon, D. J.: GHGSat-C1-Initial Results,
Design, and Characterization, in: vol. 2020, AGU Fall Meeting Abstracts, 1–17 December 2020, virtual, A247-03, https://ui.adsabs.harvard.edu/abs/2020AGUFMA247...03R/abstract (last access: 20 April 2023), 2020. a, b
Sadavarte, P., Pandey, S., Maasakkers, J. D., Lorente, A., Borsdorff, T.,
Denier van der Gon, H., Houweling, S., and Aben, I.: Methane Emissions from
Superemitting Coal Mines in Australia Quantified Using TROPOMI Satellite
Observations, Environ. Sci. Technol., 55, 16573–16580,
https://doi.org/10.1021/acs.est.1c03976, 2021. a, b, c
Sánchez-García, E., Gorroño, J., Irakulis-Loitxate, I., Varon, D. J., and Guanter, L.: Mapping methane plumes at very high spatial resolution with the WorldView-3 satellite, Atmos. Meas. Tech., 15, 1657–1674, https://doi.org/10.5194/amt-15-1657-2022, 2022. a
Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020. a, b, c
Scarpelli, T. R. and Jacob, D. J.: Global Fuel Exploitation Inventory (GFEI),
Harvard Dataverse [data set], https://doi.org/10.7910/DVN/HH4EUM, 2022. a
Scarpelli, T. R., Jacob, D. J., Grossman, S., Lu, X., Qu, Z., Sulprizio, M. P., Zhang, Y., Reuland, F., Gordon, D., and Worden, J. R.: Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: Evaluation with inversions of atmospheric methane observations, Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, 2022a. a
Schneising, O., Buchwitz, M., Reuter, M., Vanselow, S., Bovensmann, H., and
Burrows, P. J.: Remote sensing of methane leakage from natural gas and
petroleum systems revisited, Atmos. Chem. Phys., 20, 9169–9182, https://doi.org/10.5194/acp-20-9169-2020, 2020. a, b
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., and Xu, X.: DBSCAN
revisited, revisited: Why and how you should (still) use DBSCAN, ACM
T. Database Syst., 42, 19, https://doi.org/10.1145/3068335, 2017. a
Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., Van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data] (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.8087134, 2023a. a
Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., Van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Interactive map with TROPOMI and high-resolution scenes [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data] (1.0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.8355808, 2023b. a
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra,
D.: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based
Localization, Int. J. Comput. Vis., 128, 336–359, https://doi.org/10.1007/s11263-019-01228-7, 2020. a, b, c, d
Shaw, J. T., Allen, G., Barker, P., Pitt, J. R., Pasternak, D., Bauguitte,
S. J., Lee, J., Bower, K. N., Daly, M. C., Lunt, M. F., Ganesan, A. L.,
Vaughan, A. R., Chibesakunda, F., Lambakasa, M., Fisher, R. E., France,
J. L., Lowry, D., Palmer, P. I., Metzger, S., Parker, R. J., Gedney, N.,
Bateson, P., Cain, M., Lorente, A., Borsdorff, T., and Nisbet, E. G.: Large
Methane Emission Fluxes Observed From Tropical Wetlands in Zambia, Global
Biogeochem. Cy., 36, e2021GB007261, https://doi.org/10.1029/2021GB007261, 2022. a
Shen, L., Gautam, R., Omara, M., Zavala-Araiza, D., Maasakkers, J. D.,
Scarpelli, T. R., Lorente, A., Lyon, D., Sheng, J., Varon, D. J., Nesser, H.,
Qu, Z., Lu, X., Sulprizio, M. P., Hamburg, S. P., and Jacob, D. J.:
Satellite quantification of oil and natural gas methane emissions in the US
and Canada including contributions from individual basins, Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, 2022. a, b
Sherwin, E. D., Rutherford, J. S., Chen, Y., Aminfard, S., Kort, E. A.,
Jackson, R. B., and Brandt, A. R.: Single-blind validation of space-based
point-source methane emissions detection and quantification, Sci. Rep., 13, 3836, https://doi.org/10.1038/s41598-023-30761-2, 2023. a, b, c
Simonyan, K. and Zisserman, A.: Very Deep Convolutional Networks for
Large-Scale Image Recognition, arxiv [preprint], https://doi.org/10.48550/ARXIV.1409.1556, 2014. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J.,
Wang, W., Powers, J. G., Duda, M. G., and Barker, D. M.: A description of
the advanced research WRF model version 4, Tech. rep., National Center for
Atmospheric Research, Boulder, CO, USA, https://doi.org/10.5065/1dfh-6p97, 2019. a, b
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014. a
SRON CH4 L2 team: SRON RemoTeC-S5P scientific
Szopa, S., Naik, V., Adhikary, B., Artaxo, P., Berntsen, T., Collins, W.,
Fuzzi, S., Gallardo, L., Kiendler-Scharr, A., Klimont, Z., Liao, H., Unger, N., and Zanis, P.: Short-Lived Climate Forcers, Cambridge University Press, Cambridge, UK and New York, NY, USA, 817–922, https://doi.org/10.1017/9781009157896.008, 2021. a, b
Tu, Q., Hase, F., Schneider, M., Garciá, O., Blumenstock, T., Borsdorff, T., Frey, M., Khosrawi, F., Lorente, A., Alberti, C., Bustos, J. J., Butz,
A., Carreño, V., Cuevas, E., Curcoll, R., Diekmann, C. J., Dubravica, D., Ertl, B., Estruch, C., León-Luis, S. F., Marrero, C., Morgui, J. A., Ramos, R., Scharun, C., Schneider, C., Sepúlveda, E., Toledano, C., and Torres, C.: Quantification of CH4 emissions from waste disposal sites near the city of Madrid using ground- and space-based observations of COCCON, TROPOMI and IASI, Atmos. Chem. Phys., 22, 295–317, https://doi.org/10.5194/acp-22-295-2022, 2022a. a, b
Tu, Q., Schneider, M., Hase, F., Khosrawi, F., Ertl, B., Necki, J., Dubravica, D., Diekmann, C. J., Blumenstock, T., and Fang, D.: Quantifying CH4 emissions in hard coal mines from TROPOMI and IASI observations using the wind-assigned anomaly method, Atmos. Chem. Phys., 22, 9747–9765, https://doi.org/10.5194/acp-22-9747-2022, 2022b. a, b
Valade, S., Ley, A., Massimetti, F., D'Hondt, O., Laiolo, M., Coppola, D.,
Loibl, D., Hellwich, O., and Walter, T. R.: Towards global volcano
monitoring using multisensor sentinel missions and artificial intelligence:
The MOUNTS monitoring system, Remote Sens., 11, 1–31, https://doi.org/10.3390/rs11131528, 2019. a
Varon, D.: Replication Data for: Automated detection and monitoring of methane super-emitters using satellite data, Harvard Dataverse [data set],
https://doi.org/10.7910/DVN/QQQ9IU, 2022. a
Varon, D. J., McKeever, J., Jervis, D., Maasakkers, J. D., Pandey, S.,
Houweling, S., Aben, I., Scarpelli, T., and Jacob, D. J.: Satellite
Discovery of Anomalously Large Methane Point Sources From Oil/Gas Production, Geophys. Res. Lett., 46, 13507–13516, https://doi.org/10.1029/2019GL083798, 2019. a, b, c, d, e, f
Veefkind, J. P., Aben, I., McMullan, K., Förster, H., de Vries, J.,
Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele,
M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann,
P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.:
TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global
observations of the atmospheric composition for climate, air quality and
ozone layer applications, Remote Sens. Environ., 120, 70–83,
https://doi.org/10.1016/j.rse.2011.09.027, 2012.
a
Yu, J., Hmiel, B., Lyon, D. R., Warren, J., Cusworth, D. H., Duren, R. M.,
Chen, Y., Murphy, E. C., and Brandt, A. R.: Methane Emissions from Natural
Gas Gathering Pipelines in the Permian Basin, Environ. Sci. Technol. Lett., 9, 969–974, https://doi.org/10.1021/acs.estlett.2c00380, 2022. a
Zavala-Araiza, D., Lyon, D., Ramón, R., Alvarez, A., Palacios, V., Harriss,
R., Lan, X., Talbot, R., and Hamburg, S. P.: Toward a Functional Definition
of Methane Super-Emitters: Application to Natural Gas Production Sites,
Environ. Sci. Technol., 49, 8167–8174, https://doi.org/10.1021/acs.est.5b00133, 2015. a, b
Zhang, Y., Gautam, R., Pandey, S., Omara, M., Maasakkers, J. D., Sadavarte, P., Lyon, D., Nesser, H., Sulprizio, M. P., Varon, D. J., Zhang, R., Houweling, S., Zavala-Araiza, D., Alvarez, R. A., Lorente, A., Hamburg, S. P., Aben, I., and Jacob, D. J.: Quantifying methane emissions from the largest oil-producing basin in the United States from space, Sci. Adv., 6,
1–10, https://doi.org/10.1126/sciadv.aaz5120, 2020. a, b
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A.: Learning
Deep Features for Discriminative Localization, arxiv [preprint],
http://arxiv.org/abs/1512.04150 (last access: 20 April 2023), 2015. a
Short summary
Using two machine learning models, which were trained on TROPOMI methane satellite data, we detect 2974 methane plumes, so-called super-emitters, in 2021. We detect methane emissions globally related to urban areas or landfills, coal mining, and oil and gas production. Using our monitoring system, we identify 94 regions with frequent emissions. For 12 locations, we target high-resolution satellite instruments to enlarge and identify the exact infrastructure responsible for the emissions.
Using two machine learning models, which were trained on TROPOMI methane satellite data, we...
Altmetrics
Final-revised paper
Preprint